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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 1 of 33 A Refresher on Probability and Statistics Appendix C

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 2 of 33 What We’ll Do... Outline Probability – basic ideas, terminology Random variables, Statistical inference – point estimation, confidence intervals, hypothesis testing

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 3 of 33 Terminology Statistic: Science of collecting, analyzing and interpreting data through the application of probability concepts. Probability: A measure that describes the chance (likelihood) that an event will occur. In simulation applications, probability and statistics are needed to choose the input distributions of random variables, generate random variables, validate the simulation model, analyze the output.

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 4 of 33 Event: Any possible outcome or any set of possible outcomes. Sample Space: Set of all possible outcomes. Ex: How is the weather today? What is the outcome when you toss a coin? Probability of an Event: Ex: Determine the probability of outcomes when an unfair coin is tossed? Toss the coin several times (say N) under the same conditions. Event A: Head appears Terminology Frequency of event A Relative frequency of event A Define Then

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 5 of 33 Probability Basics (cont’d.) Conditional probability Knowing that an event F occurred might affect the probability that another event E also occurred Reduce the effective sample space from S to F, then measure “size” of E relative to its overlap (if any) in F, rather than relative to S Definition (assuming P(F) 0): E and F are independent if P(E F) = P(E) P(F) Implies P(E|F) = P(E) and P(F|E) = P(F), i.e., knowing that one event occurs tells you nothing about the other If E and F are mutually exclusive, are they independent?

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 6 of 33 Random Variables One way of quantifying, simplifying events and probabilities A random variable (RV) is a number whose value is determined by the outcome of an experiment Technically, a function or mapping from the sample space to the real numbers, but can usually define and work with a RV without going all the way back to the sample space Think: RV is a number whose value we don’t know for sure but we’ll usually know something about what it can be or is likely to be Usually denoted as capital letters: X, Y, W 1, W 2, etc. Probabilistic behavior described by distribution function

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 7 of 33 Random Variables Random Var: is a real and single valued function f(E): S R defined on each element E in the sample space S. event1 event2. eventn Thus for each event there is a corresponding random variable F( E ) R

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 8 of 33 Ex: In a simulation study how do we decide whether a customer is smoker or not? Let P( Smoker ) = 0.3 and P( Nonsmoker ) = 0.7 Generate a random number between [0,1] If it is < 0.3 smoker else nonsmoker Random Variables in Simulation

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 9 of 33 Discrete vs. Continuous RVs Two basic “flavors” of RVs, used to represent or model different things Discrete – can take on only certain separated values Number of possible values could be finite or infinite Continuous – can take on any real value in some range Number of possible values is always infinite Range could be bounded on both sides, just one side, or neither

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 10 of 33 Discrete Random Variables Probability Mass Function is defined as 1/3 1/6 P(X) X Ex: Demand of a product, X has the following probability function X1X1 X4X4 X3X3 X2X2

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 11 of 33 Cumulative distribution function, F(x) is defined as where F(x) is the distribution or cumulative distribution function of X. 12 34 1 5/6 1/2 1/6 F(x) x Ex: Discrete Random Variables

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 12 of 33 Expected Value of a Discrete R.V. Data set has a “center” – the average (mean) RVs have a “center” – expected value, What expectation is not: The value of X you “expect” to get! E(X) might not even be among the possible values x 1, x 2, … What expectation is: Repeat “the experiment” many times, observe many X 1, X 2, …, X n E(X) is what converges to (in a certain sense) as n , where

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 13 of 33 Variances and Standard Deviation of a Discrete R.V. Data set has measures of “dispersion” – Sample variance Sample standard deviation RVs have corresponding measures Weighted average of squared deviations of the possible values xi from the mean. Standard deviation of X is

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 14 of 33 Continuous Distributions Now let X be a continuous RV Possibly limited to a range bounded on left or right or both. No matter how small the range, the number of possible values for X is always (uncountably) infinite Not sensible to ask about P(X = x) even if x is in the possible range P(X = x) = 0 Instead, describe behavior of X in terms of its falling between two values!

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 15 of 33 Continuous Distributions (cont’d.) Probability density function (PDF) is a function f(x) with the following three properties: f(x) 0 for all real values x The total area under f(x) is 1: Although P(X=x)=0, Fun facts about PDFs Observed X’s are denser in regions where f(x) is high The height of a density, f(x), is not the probability of anything – it can even be > 1 With continuous RVs, you can be sloppy with weak vs. strong inequalities and endpoints

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 16 of 33 Cumulative distribution function Let I = [a,b] F(x) X 1 Continuous Random Variables

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 17 of 33 Ex: Lifetime of a laser ray device used to inspect cracks in aircraft wings is given by X, with pdf X (years) 1/2 2 3 Continuous Random Variables

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 18 of 33 Continuous Expected Values, Variances, and Standard Deviations Expectation or mean of X is Roughly, a weighted “continuous” average of possible values for X Same interpretation as in discrete case: average of a large number (infinite) of observations on the RV X Variance of X is Standard deviation of X is

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 19 of 33 Independent RVs X 1 and X 2 are independent if their joint CDF factors into the product of their marginal CDFs: Equivalent to use PMF or PDF instead of CDF Properties of independent RVs: They have nothing (linearly) to do with each other Independence uncorrelated – But not vice versa, unless the RVs have a joint normal distribution Important in probability – factorization simplifies greatly Tempting just to assume it whether justified or not Independence in simulation Input: Usually assume separate inputs are indep. – valid? Output: Standard statistics assumes indep. – valid?!?!?!?

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 20 of 33 Sampling Statistical analysis – estimate or infer something about a population or process based on only a sample from it Think of a RV with a distribution governing the population Random sample is a set of independent and identically distributed (IID) observations X 1, X 2, …, X n on this RV In simulation, sampling is making some runs of the model and collecting the output data Don’t know parameters of population (or distribution) and want to estimate them or infer something about them based on the sample

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 21 of 33 Sampling (cont’d.) Population parameter Population mean = E(X) Population variance 2 Population proportion Parameter – need to know whole population Fixed (but unknown) Sample estimate Sample mean Sample variance Sample proportion Sample statistic – can be computed from a sample Varies from one sample to another – is a RV itself, and has a distribution, called the sampling distribution

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 22 of 33 Sampling Distributions Have a statistic, like sample mean or sample variance Its value will vary from one sample to the next Some sampling-distribution results Sample mean If Regardless of distribution of X, Sample variance s 2 E(s 2 ) = 2 Sample proportion E( ) = p

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 23 of 33 Point Estimation A sample statistic that estimates (in some sense) a population parameter Properties Unbiased: E(estimate) = parameter Efficient: Var(estimate) is lowest among competing point estimators Consistent: Var(estimate) decreases (usually to 0) as the sample size increases

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 24 of 33 Confidence Intervals A point estimator is just a single number, with some uncertainty or variability associated with it Confidence interval quantifies the likely imprecision in a point estimator An interval that contains (covers) the unknown population parameter with specified (high) probability 1 – Called a 100 (1 – )% confidence interval for the parameter Confidence interval for the population mean :

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 25 of 33 Confidence Intervals in Simulation Run simulations, get results View each replication of the simulation as a data point Random input random output Form a confidence interval If you observe the system infinitely many times, 100 (1 – )% of the time this inerval will contain the true population mean!

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 26 of 33 Hypothesis Tests Test some assertion about the population or its parameters Can never determine truth or falsity for sure – only get evidence that points one way or another Null hypothesis (H 0 ) – what is to be tested Alternate hypothesis (H 1 or H A ) – denial of H 0 H 0 : = 6 vs. H 1 : 6 H 0 : < 10 vs. H 1 : 10 H 0 : 1 = 2 vs. H 1 : 1 2 Develop a decision rule to decide on H 0 or H 1 based on sample data

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 27 of 33 Errors in Hypothesis Testing

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 28 of 33 p-Values for Hypothesis Tests Traditional method is “Accept” or Reject H 0 Alternate method – compute p-value of the test p-value = probability of getting a test result more in favor of H 1 than what you got from your sample Small p (like < 0.01) is convincing evidence against H 0 Large p (like > 0.20) indicates lack of evidence against H 0 Connection to traditional method If p < , reject H 0 If p , do not reject H 0 p-value quantifies confidence about the decision

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Simulation with ArenaAppendix C – A Refresher on Probability and StatisticsSlide 29 of 33 Hypothesis Testing in Simulation Input side Specify input distributions to drive the simulation Collect real-world data on corresponding processes “Fit” a probability distribution to the observed real-world data Test H 0 : the data are well represented by the fitted distribution Output side Have two or more “competing” designs modeled Test H 0 : all designs perform the same on output, or test H 0 : one design is better than another

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